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Social explorative attention based recommendation for content distribution platforms

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Abstract

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user’s attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users’ historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users’ higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.

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Notes

  1. Steemit (https://steemit.com/) is a blockchain based social media and decentralized content distribution platform for consumers and creators to earn Steemit tokens by playing with the platform and interacting with others. It is regarded as a more effective content distribution ecosystem that allows small content creators to share their creative contents while protecting the copyright without any intermediaries.

References

  • Abeliuk A, Berbeglia G, Hentenryck PV, Hogg T, Lerman K (2017) Taming the unpredictability of cultural markets with social influence. In: Proceedings of the 26th international conference on world wide web

  • Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd international conference on learning representations

  • Berbeglia F, Hentenryck PV (2017) Taming the matthew effect in online markets with social influence. In: Proceedings of the 31st AAAI conference on artificial intelligence

  • Chen C, Zhang M, Liu Y, Ma S (2019a) Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In: Proceedings of the 12th ACM international conference on web search and data mining

  • Chen H, Dai X, Cai H, Zhang W, Wang X, Tang R, Zhang Y, Yu Y (2019b) Large-scale interactive recommendation with tree-structured policy gradient. In: Proceedings of the 33rd AAAI conference on artificial intelligence

  • Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval

  • Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems

  • Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international conference on world wide web

  • Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Proceedings of the 28th international conference on world wide web

  • Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining

  • He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web

  • Hu GN, Dai XY, Song Y, Huang S, Chen J (2015) A synthetic approach for recommendation: combining ratings, social relations, and reviews. In: Proceedings of the 24th international joint conference on artificial intelligence

  • Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE international conference on data mining

  • Huang PS, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on information & knowledge management

  • Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM conference on recommender systems

  • Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, Yang S (2012) Social contextual recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management

  • Joachims T, Freitag D, Mitchell TM (1997) Web watcher: A tour guide for the world wide web. In: Proceedings of the 15th international joint conference on artificial intelligence

  • Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing

  • Kocsis L, Szepesvári C (2006) Bandit based monte-carlo planning. In: European conference on machine learning

  • Koehn P (2004) Pharaoh: a beam search decoder for phrase-based statistical machine translation models. In: Conference of the association for machine translation in the Americas

  • Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8)

  • Lee J, Kim S, Lebanon G, Singer Y, Bengio S (2016) Llorma: local low-rank matrix approximation. J Mach Learn Res 17(1):442–465

    MathSciNet  MATH  Google Scholar 

  • Li L, Chu W, Langford J, Schapire RE (2010a) A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th international conference on world wide web

  • Li W, Wang X, Zhang R, Cui Y, Mao J, Jin R (2010b) Exploitation and exploration in a performance based contextual advertising system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining

  • Li X, She J (2017) Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Liebman E, Khandelwal P, Saar-Tsechansky M, Stone P (2017) Designing better playlists with monte carlo tree search. In: Proceedings of the 31st AAAI conference on artificial intelligence

  • Liu J, Dolan P, Pedersen ER (2010) Personalized news recommendation based on click behavior. In: Proceedings of the 15th international conference on intelligent user interfaces

  • Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing

  • Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the 4th ACM international conference on Web search and data mining

  • McInerney J, Lacker B, Hansen S, Higley K, Bouchard H, Gruson A, Mehrotra R (2018) Explore, exploit, and explain: personalizing explainable recommendations with bandits. In: Proceedings of the 12th ACM conference on recommender systems

  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily. Annu Rev Sociol 27:415–44

    Article  Google Scholar 

  • Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533

    Article  Google Scholar 

  • Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Proceedings of the 27th Conference on neural information processing systems

  • Radlinski F, Kleinberg R, Joachims T (2008) Learning diverse rankings with multi-armed bandits. In: Proceedings of the 25th international conference on machine learning

  • Rendle S (2012) Factorization machines with libfm. ACM Trans Intell Syst Technol 3(3):57

    Article  Google Scholar 

  • Rigney D (ed) (2010) The matthew effect, how advantage begets further advantage. Columbia University Press, New York

    Google Scholar 

  • Salganik MJ, Dodds PS, Watts DJ (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762):854–856

    Article  Google Scholar 

  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529:484–503

    Article  Google Scholar 

  • Song W, Xiao Z, Wang Y, Charlin L, Zhang M, Tang J (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the 12th ACM international conference on web search and data mining

  • Sun P, Wu L, Wang M (2018) Attentive recurrent social recommendation. In: Proceedings of the 41st international ACM SIGIR conference on research & development in information retrieval

  • Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph Attention networks. In: Proceedings of the 6th international conference on learning representations

  • Wang H, Chen B, Li WJ (2013) Collaborative topic regression with social regularization for tag recommendation. In: Proceedings of the 23rd international joint conference on artificial intelligence

  • Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining

  • Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018a) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management

  • Wang H, Zhang F, Xie X, Guo M (2018b) Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 27th international conference on world wide web

  • Wang X, Wang Y, Hsu D, Wang Y (2014) Exploration in interactive personalized music recommendation: a reinforcement learning approach. ACM Trans Multimedia Comput Commun Appl 11(1):7

    Google Scholar 

  • Wang X, Yu L, Ren K, Tao G, Zhang W, Yu Y, Wang J (2017) Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Wang X, He X, Cao Y, Liu M, Chua TS (2019a) Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Wang X, Zhu W, Liu C (2019b) Social recommendation with optimal limited attention. In: Proceedings of the 25rd ACM sigkdd international conference on knowledge discovery and data mining

  • Wei YZ, Moreau L, Jennings NR (2005) A market-based approach to recommender systems. ACM Trans Inf Syst 23(3):227–266

    Article  Google Scholar 

  • Wu C, Wu F, An M, Huang J, Huang Y, Xie X (2019a) Npa: Neural news recommendation with personalized attention. In: Proceedings of the 25rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Wu L, Sun P, Fu Y, Hong R, Wang X, Wang M (2019b) A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval

  • Xiang B, Liu Q, Chen E, Xiong H, Zheng Y, Yang Y (2013) Pagerank with priors: an influence propagation perspective. In: Proceedings of the 33rd international joint conference on artificial intelligence

  • Xiao J, Ye H, He X, Zhang H, Wu F, Chua TS (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the 37th international joint conference on artificial intelligence

  • Xiao W, Zhao H, Pan H, Song Y, Zheng VW, Yang Q (2019) Beyond personalization: Social content recommendation for creator equality and consumer satisfaction. In: Proceedings of the 25rd ACM SIGKDD international conference on knowledge discovery and data mining

  • Xu F, Lian J, Han Z, Li Y, Xu Y, Xie X (2019) Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In: Proceedings of the 28th ACM international conference on information and knowledge management

  • Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of the 32nd international conference on international conference on machine learning

  • Yang X, Steck H, Liu Y (2012) Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining

  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies

  • Ye M, Liu X, Lee WC (2012) Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

  • Zhao H, Yao Q, Kwok JT, Lee DL (2017) Collaborative filtering with social local models. In: 2017 IEEE international conference on data mining (ICDM)

  • Zhao H, Zhou Y, Song Y, Lee DL (2019) Motif enhanced recommendation over heterogeneous information network. In: Proceedings of the 28th ACM international conference on information and knowledge management

  • Zheng G, Zhang F, Zheng Z, Xiang Y, Yuan NJ, Xie X, Li Z (2018) Drn: A deep reinforcement learning framework for news recommendation. In: Proceedings of the 27th international conference on world wide web

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Acknowledgements

The authors of this paper were supported by NSFC (U20B2053), Hong Kong RGC including Early Career Scheme (ECS, No. 26206717), General Research Fund (GRF, No. 16211520), and Research Impact Fund (RIF, No. R6020-19), and WeBank-HKUST Joint Lab. This article was partially done when the first author was an intern at WeBank AI Department. We also thank the anonymous reviewers for their valuable comments and suggestions that help improve the quality of this manuscript.

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Authors and Affiliations

  1. HKUST, Hong Kong, China

    Wenyi Xiao, Haojie Pan & Yangqiu Song

  2. 4Paradigm Inc., Beijing, China

    Huan Zhao

  3. Peng Cheng Laboratory, Shenzhen, China

    Yangqiu Song

  4. WeBank, Shenzhen, China

    Vincent W. Zheng & Qiang Yang

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  1. Wenyi Xiao
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  2. Huan Zhao
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Correspondence to Wenyi Xiao.

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Xiao, W., Zhao, H., Pan, H. et al. Social explorative attention based recommendation for content distribution platforms. Data Min Knowl Disc 35, 533–567 (2021). https://doi.org/10.1007/s10618-020-00729-1

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